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REFERENCES

1 
Amarasinghe K., Marino D.L., Manic M., June 2017, Deep neural networks for energy load forecasting, in IEEE 26th International Symposium on Industrial ElectronicsDOI
2 
Marino D.L., Amarasinghe K., Manic M., Oct. 2016, Building energy load forecasting using Deep Neural Networks, in IEEE Industrial Electronics SocietyDOI
3 
Azadeh A., Faiz Z.S., 2011, A meta-heuristic framework for forecasting household electricity consumption, in Elsevier of Applied Soft Computing, Vol. 11, No. 1, pp. 614-620DOI
4 
Mpawenimana I., Pegatoquet A., Roy V., Rodrguez L., Belleudy C., Oct. 2020, A comparative study of LSTM and ARIMA for energy load prediction with enhanced data preprocessing, in IEEE Sensors Applications SymposiumDOI
5 
Taylor J.W., McSharry P.E., Nov. 2007, Short-Term Load Forecasting Methods: An Evaluation Based on European Data, in IEEE Transactions on Power Systems, Vol. 22DOI
6 
Mpawenimana I., Pegatoquet A., Roy V., Rodriguez L., Belleudy C., 2020, A comparative study of LSTM and ARIMA for energy load prediction with enhanced data preprocessing, in IEEE Sensors Applications SymposiumDOI
7 
Tian H., Meng B., 2010, A new modeling method based on bagging ELM for day-ahead electricity price prediction, in IEEE International Conference on Bio-Inspired ComputingDOI
8 
Sheikhan M., Mohammadi N., 2012, Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection, in Neural Computing and Applications 21, pp. 1961-1970DOI
9 
Fan L., Li J., Zhang X., Sep. 2020, Load Prediction Methods Using Machine Learning for Home Energy Management Systems Based on Human Behavior Patterns Recognition, in CSEE Journal of Power and Energy Systems, Vol. 6, No. 3DOI
10 
Taylor J.W., Menezes L.D , McSharry P.E., Jan.-Mar. 2006, A Comparison of univariate methods for forecasting electricity demand up to a day ahead, in International Journal of Forecasting, Vol. 22, No. 1, pp. 1-16DOI
11 
OpenWeather, websiteURL
12 
Bedi J., Toshniwal D., 2019, Deep learning framework to forecast electicity demand, in Elsevier of Applied Energy, Vol. 238, pp. 1312-1326DOI
13 
Taylor J.W., 2003, Short-term electricity demand forecasting using double seasonal exponential smoothing, in Journal of the Operational Research Society, Vol. 54, No. 8DOI
14 
Mirasgedis S., Sarafidis Y., Georgopoulou E., Lalas D.P., Moschovits M., Karagiannis F., Papakonstantinou D., 2006, Models for mid-term electricity demand forecasting incorporating weather influences, in Elsevier of Energy, Vol. 31, No. 2-3, pp. pages 208-227DOI
15 
Wijiaya T.K., Vasirani M., Humeau S., Aberer K., 2015, Cluster-based aggregate forecasting for residential electricity demand using smart meter data, in IEEE International Conference on Big Data 10.1109/BigData.2015.7363836DOI
16 
Almeida M.P., Perpinan O., Narvarte L., 2015, PV power forecast using a nonparametric PV model, in Elsevier of Solar Energy, Vol. 115, pp. 354-368DOI
17 
Qamar M., Nadarajah M., Ekanayake C., 2016, On recent advances in PV output power forecast, in Elsevier of Solar Energy, Vol. 136, pp. 125-144DOI
18 
Sanjari M.J., Gooi H.B., 2017, Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain, in IEEE Transactions on Power Systems, Vol. 32, No. 4DOI
19 
Das U.K., Tey K.S., Seyedmahmoudian M., Idris M.Y.I., Mekhilef S., Horan B., Stojcevski A., 2017, SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions, in MDPI of energiesDOI
20 
Meer D., Mouli G.R.C., Mouli G.M., Elizondo L.R., Bauer P., 2018, Energy Management System With PV Power Forecast to Optimally Charge EVs at the Workplace, in IEEE Transactions on Industrial Informatics, Vol. 14, No. 1DOI
21 
Zhang G.P., 2003, Time series forecasting using a hybrid ARIMA, neural network model, in Elsevier of Neurocomputing, Vol. 50, pp. 159-175DOI
22 
Contreras J., Espinola R., Nogales F.J., Conejo A.J., 2003, ARIMA models to predict next-day electricity prices, in IEEE Transactions on Power Systems, Vol. 18, No. 3DOI
23 
Ho S.L., Xie M., 1998, The use of ARIMA models for reliability forecasting and analysis, in Elsevier of Computers & Industrial Engineering, Volume 35, Vol. 1, No. 2, pp. 213-216DOI
24 
Chiu J.P.C., Nichols E., 2016, Named Entity Recognition with Bidirectional LSTM-CNNs, in Transactions of the Association for Computational LinguisticsDOI
25 
C.N. dos Santos , Guimaraes V., 2015, Boosting Named Entity Recognition with Neural Character Embeddings, in arXiv:1505.05008URL
26 
Dietterich T.G., 2000, Ensemble Methods in Machine Learning, in International Workshop on Multiple Classifier Systems, pp. 1-15DOI
27 
Peng L., Liu S., Liu R., Wang L., 2018, Effective long short-term memory with differential evolution algorithm for electricity price prediction, in Elsevier of Energy, Vol. 162, pp. 1201-1314DOI
28 
Chang Z., Zhang Y., Chen W., 2019, Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform, in Elsevier of Energy, Vol. 187DOI
29 
Niimura T., Ko H.S., Ozawa K., 2002, A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market, in IEEE Proceedings of the International Joint Conference on Neural NetworksDOI